CUDA_VISIBLE_DEVICES=5 python3 main_pretrain.py \
    --dataset imagenet100 \
    --backbone resnet50 \
    --train_data_path datasets/imagenet100/train \
    --val_data_path datasets/imagenet100/val \
    --max_epochs 400 \
    --devices 0\
    --accelerator gpu \
    --strategy ddp \
    --sync_batchnorm \
    --precision 16 \
    --optimizer sgd \
    --scheduler warmup_cosine \
    --lr 0.5 \
    --classifier_lr 0.1 \
    --weight_decay 1e-5 \
    --batch_size 128 \
    --num_workers 4 \
    --brightness 0.4 \
    --contrast 0.4 \
    --saturation 0.4 \
    --hue 0.1 \
    --num_crops_per_aug 2 \
    --zero_init_residual \
    --name simsiam-400ep-imagenet100 \
    --data_format dali \
    --entity zhangq327 \
    --project solo-learn \
    --wandb \
    --save_checkpoint \
    --auto_resume \
    --method simsiam \
    --proj_hidden_dim 2048 \
    --pred_hidden_dim 512 \
    --proj_output_dim 2048\
    --data_format image_folder
